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Side Effects in programming

According to Wikipedia, a function or expression is said to have a side effect if it modifies some state outside its local environment. Let’s understand this in depth by examples.

To understand side effects, first, we should know what “state” is. In layman terms, the state refers to the value of the variable at any given point in the program like x=2.

Now, the above Wikipedia definition might have started to make some sense. If not, don’t worry, see the figure below.

In this figure, we have a variable x that is outside the scope of two functions. Clearly, after the Function_2 call, the value of x changes which means that Function_2, other than returning value(if any), is interacting with variables outside its local environment and thus has a side effect.

In general, any lasting effect that occurs in a function, not through its return value, is called a side effect. Not through its return value means if we assign x=Function_1(), this will clearly change the value of x but this is not a side effect. For side effect, the change should occur inside a function.

Common ways to have side effects:

Modifying a non-local variable: Let’s understand by an example

x=3
def square(x):
    x=x*x
    return x
def square_side_effect():
    global x
    x=x*x
    return x
print(x)                 # 3
print(square(x))         # 9
print(x)                 # 3 
print(square_side_effect())     # 9
print(x)                 # 9

x=3

def square(x):

x=x*x

return x

def square_side_effect():

global x

x=x*x

return x

print(x) # 3

print(square(x)) # 9

print(x) # 3

print(square_side_effect()) # 9

print(x) # 9

The x in the square() function is local to its scope so no change in outside x. Global in square_side_effect() makes x global so any change in x will change it in whole code (outside function scope) so the square_side_effect function() is not pure(has side effects).

Changing the value of a mutable object: Let’s take Lists which is a mutable object.

lst = [1,2,'Pankaj']
def list_side_effect(lst):
    lst[1] = 1
    lst[2] = 'Kang'
    return lst
print(lst)             # [1,2,'Pankaj']
list_side_effect(lst)
print(lst)             # [1,1,'Kang']

lst = [1,2,'Pankaj']

def list_side_effect(lst):

lst[1] = 1

lst[2] = 'Kang'

return lst

print(lst) # [1,2,'Pankaj']

list_side_effect(lst)

print(lst) # [1,1,'Kang']

Clearly, after calling list_side_effect(), the value of lst changes so, list_side has side effects.

Performing Input/Output operations: This doesn’t change any objects or variable value, but it does have a potential lasting effect outside the function call because a person might see the output and be influenced by it.
Calling other side-effect functions

Now, you might have got some feeling about the side effects. Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.

Functional programming paradigm

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Functional programming is based on Lambda Calculus, a computational model proposed by Alonzo Church in the 1930’s. This paradigm falls under the declarative umbrella rather than imperative, meaning, programming is done with expressions or declarations instead of statements. (Relax… See below the properties and you will understand what this means)

Note: We can do programming in a functional style in languages that are not specifically designed for functional programming. C++, Java, Python etc. all added constructs to facilitate the functional style. So, programmers using “mostly imperative” languages may have utilized some of the functional concepts.

Pros: more concise, more predictable, and easier to test than imperative or object-oriented code only if you understand.

Main Concepts of Functional Programming:

Pure Functions: A pure function is a function which follows given properties like

For the same input, it always returns the same output.
Has no side effects(For details check the link).
Referential Transparency.

Although, these properties are somehow correlated. Like, a function can only return the same output for the same input, only if it has no side effects. Let’s see by examples

def square(x):
    return x*x

1 2	def square(x): return x*x

The square function always gives the same output for the same x .

def random_num():
    return np.random.randn()

1 2	def random_num(): return np.random.randn()

Even though we didn’t pass any arguments to the random_num function, it gives different output, meaning that random_num is not pure.

Now, let’s look at the side effect property.
Side effect is any state(value of the variable) change outside the called function scope. In simple terms, if a function changes the state/value of the variable that is outside function scope or local environment then the function/unit of code is said to have a side effect.

x=3
def square(x):
    x=x*x
    return x
def square_side():
    global x
    x=x*x
    return x
print(x)                 # 3
print(square(x))         # 9
print(x)                 # 3 
print(square_side())     # 9
print(x)                 # 9

x=3

def square(x):

x=x*x

return x

def square_side():

global x

x=x*x

return x

print(x) # 3

print(square(x)) # 9

print(x) # 3

print(square_side()) # 9

print(x) # 9

Clearly, the x value changes after square_side() function which means that this function has side effects. The x in the square() function is local to its scope so no change in outside x. Global in square_side() makes x global so any change in x will change it in whole code (outside function scope) so the square_side function is not pure(has side effects).

Another important property of pure functions is Referential Transparency means we can replace a function call with its resulting value without changing the meaning of the program. Let’s see by an example

def double(x):
    return x*2
y = double(2) # or y = 4

def double(x):

return x*2

y = double(2) # or y = 4

In the above example, we can easily replace double(2) by 4 because we know that this function always gives the same result for the same input(No side effects). This is Referential Transparency.

First Class functions: “first-class” is a computer science term that describes something that has no restriction on its use. Thus, first-class functions mean it can appear anywhere in the program like as arguments to other functions and as their return values.
Recursion: Recursion means letting an operation be repeated until it reaches the base case. In the functional paradigm, we do not use the loops and all the work is done by recursion.

Python

def factorial(n): if n == 1: return n else: return n*factorial(n-1)

1
2
3
4
5

def factorial(n):
   if n == 1:
       return n
   else:
       return n*factorial(n-1)
Avoid Shared State: A shared state is a state that is shared between more than one function or more than one data structure and you should avoid creating functions that rely on the same variable.

Now, you might have got some feeling about the Functional programming. Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.

Procedural Programming paradigm

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Procedural programming, as the name suggests, is based on the concept of procedural call. To understand it more deeply, we should first know what procedural call means.

Procedures, also known as routines, subroutines, or functions, simply contain a series of computational steps to be carried out. Let’s see an example of the procedure

#Procedure
def add_list(my_list):
    Total = 0
    for x in my_list:
       Total += x  
    return Total        
# Procedure Call
add_list([1,2,3,4])

#Procedure

def add_list(my_list):

Total = 0

for x in my_list:

Total += x

return Total

# Procedure Call

add_list([1,2,3,4])

Procedure calls are modular and are bound by scope. It means we break the program into independent, interchangeable small pieces known as modules such that each module contains everything necessary to execute. Each module is composed of one or more procedures.

Procedural programs may possibly have multiple levels or scopes, with subprograms defined inside other subprograms. Scoping helps to keep the procedures modular and prevents/allows the procedure, from accessing the variables of other procedures.

Procedural programming falls under the imperative umbrella which means that procedural inherits the properties of the imperative paradigm. Procedural programming uses a linear or top-down approach.

Pros: easier to read and more maintainable, more flexible, allows modules to be used again in the form of code libraries.

Cons: this approach may not use hardware efficiently when tackling complex problems because of side effects (may not run parallelly)

In short, procedural programming means breaking the program into modules and using the imperative approach. If we take away the imperative nature from procedural, then it becomes same as Functional.

Now, you might have got some feeling about the Procedural programming. Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.

Imperative vs Declarative programming paradigm

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In this post, we will be looking at the major differences between imperative and declarative programming paradigms.

Imperative Programming paradigm: The languages that fall into this category have 2 main properties:

Control flow is explicit meaning how the operation took place, step by step or more naively “First do this then do that”.Thus, the order of steps is crucial.
Allow side effects.

To understand side effects, first, we should know what “state” is. In layman terms, the state refers to the value of the variable at any given point in the program like x=2.

Side effect is any state change outside the called function scope. In simple terms, if a function changes the state that is outside function scope or local environment then the function/unit of code is said to have a side effect.

Examples of function side effect include modifying any external variable or object property, performing I/O, calling functions having side effects etc.

Total = 0
Fare=[10,20,30,40]
for x in Fare:
    Total += x
mean_fare=Total/len(Fare)
print(mean_fare)

Total = 0

Fare=[10,20,30,40]

for x in Fare:

Total += x

mean_fare=Total/len(Fare)

print(mean_fare)

Let’s see how this code follows the properties of imperative paradigm. First, the order of steps is crucial because if we implement “mean_fare” statement before the loop, it will give a different result. Second, the unit of code i.e. loop, has side effects as it changes the state of the “Total” variable which is outside its scope.

I hope this might have given you the insight of the imperative programming paradigm.

Now, let’s look at some contradictions that I found while searching for this stuff

Some people might argue that the above code belongs to structured programming which is a kind of imperative programming. The main difference between these two is that in the imperative, control flow is defined by the collection of “goto” statements while in the structured, control flow is defined by nested loops, conditionals, and subroutines with no “goto” statements. okay that’s good I said, but then they categorized “Python” into imperative paradigm knowing that it doesn’t have “goto” statement.

So, what I found is that there are terms like structured and non-structured imperative programming. Above code falls into the structured imperative paradigm while if we use the goto statement to define control flow then it falls under the non-structured imperative paradigm.

Declarative Programming paradigm: This is defined as any style of programming that is not imperative. So, the properties will just be the opposite of imperative.

Sequence of the steps is not crucial.
Don’t allow Side effects.

In general, the imperative focuses on “how to do” while declarative focuses on “what to do”meaning logic of software without actually describing its flow. Let’s look at the code below to get more insights into this:

def Mean_list(lst):
    return sum(lst)/len(lst)

1 2	def Mean_list(lst): return sum(lst)/len(lst)

Using reduce() and lambda functions

from functools import reduce
def Mean_list(lst):
    return reduce(lambda a, b: a + b, lst) / len(lst)

from functools import reduce

def Mean_list(lst):

return reduce(lambda a, b: a + b, lst) / len(lst)

Note: Since python in no sense is declarative, the above examples are more towards functional side than declarative. But since functional also falls under the declarative umbrella, above examples can serve as a good platform for understanding declarative concepts.

Explanation: In the above examples, we are describing “WHAT” we want to do rather than HOW (we don’t know HOW lambda and reduce are implemented, we also don’t care). We’re not mutating any state. All of the mutations are abstracted inside of “lambda” and “reduce“. It’s also more readable (once you get used to “lambda” and “reduce“, of course).

Now, you might have got some feeling about the “How to do” and “What to do” approach. Later, this will become more clear when we will go through Functional and other paradigms. Till then, Enjoy.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.

Programming paradigms

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In this posts series, we will be looking at the major programming paradigms and then, at last, we will see which paradigm does python follow.

Hi, have you ever wondered how many programming languages are there? Surfing through the net I found out that there are approx. 256 programming languages till now and still people are working. These languages may have their own pros based on code readability, efficiency or these may be good for different purposes.

But there is one similarity between these languages, that they follow some programming paradigm. We can classify these languages based on which programming paradigm they follow(although many people don’t agree as most of the languages include features from several paradigms so, difficult to classify).

A programming paradigm refers to the style of programming, meaning how you are building the structure and elements of the program. (ufff… Don’t panic from this innocent looking definition, for now just remember that this means some model with a set of rules/features ).There are several features that determine a programming paradigm such as modularity, objects, interrupts or events, control flow etc.

Everyone has an opinion on which programming paradigm/coding style is the best. Sometimes, a single style doesn’t truly solve the problem and you may have to combine different styles on the same problem.

The languages that follow a single paradigm are called “Pure” like Haskell while others are multi-paradigm like python, Java etc.

Common programming paradigms can be “roughly” categorized as:

Although this is not the right way to categorize paradigms. Only for understanding, these paradigms are grouped based on some similar properties. You can see the detailed classification given by Peter Van Roy.

In the next posts, we will look at these paradigms one by one. Till then, Enjoy.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.

TheAILearner

Mastering Artificial Intelligence

Tag Archives: programming paradigm

Side Effects in programming

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Functional programming paradigm

Procedural Programming paradigm

Imperative vs Declarative programming paradigm

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Programming paradigms

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